New PCA-Based Index Improves Weather File Selection for Tropical Daylight Modeling

Researchers Aw, Leng, and Lim have developed a new Principal Component Analysis (PCA)-based climatic similarity index to improve weather file selection for daylight modeling in tropical regions. The study, published in *Scientific Reports* in 2026, introduces this index as a tool to address existing difficulties in simulating accurate daylight conditions within tropical climates.

The researchers designed the index to refine the criteria used for selecting representative weather data, which serves as a foundation for daylight simulations. By applying PCA, the team categorized climatic variables to better align weather files with specific tropical environmental conditions. This methodology aims to provide a more precise framework for architects and engineers who rely on climate-based daylight modeling to assess building performance. The findings offer a technical approach to standardizing data selection, addressing the specific atmospheric and solar patterns characteristic of tropical zones.

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Date: June 20, 2026

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